Pedagogical Content Knowledge (PCK): A Teacher's Guide
Shulman's PCK framework explained: why subject knowledge alone is not enough. How expert teachers blend content and pedagogy to transform classroom learning.
What is Pedagogical Content Knowledge?
Pedagogical content knowledge (PCK), introduced by Lee Shulman in 1986, is the specialised knowledge that distinguishes a subject expert from an effective teacher. PCK combines deep understanding of a subject with the ability to represent it in ways learners can grasp: knowing which analogies work, which misconceptions are common, and how to sequence ideas so they build on one another. It is the bridge between knowing your subject and knowing how to teach it.
Pedagogical Content Knowledge (PCK) is a teacher's ability to blend deep subject knowledge with effective teaching methods to help students understand complex ideas. Developed by Lee Shulman in the 1980s, PCK focuses on knowing both what to teach and how to teach it in ways that make sense to specific learners. It enables teachers to anticipate student misconceptions, choose appropriate explanations, and adapt their teaching to the specific demands of the content.
Pedagogical Content Knowledge (PCK) is a concept that describes a teacher's ability to blend subject knowledge with to help students understand complex ideas. Originally introduced by educational researcher Lee Shulman in the 1980s, particularly in the context of science education, PCK has since become a key framework across all subjects and phases of teaching.
Unlike general teaching skills or expertise in a subject alone, PCK focuses on how well a teacher can anticipate student misconceptions, choose appropriate representations or explanations, and to the specific demands of the content. In essence, it's about knowing what to teach and how to teach it in a way that makes sense to learners.
Experienced teachers naturally draw on PCK during lessons, integrating techniques like questioning, concept mapping, and analogies to clarify difficult ideas. These strategies don't just deliver content; they make it meaningful, accessible, and memorable. In contrast, novice or pre-service teachers often find this more challenging, as they are still developing both their pedagogical approaches and deep understanding of subject matter.
Understanding and applying PCK allows educators to make more informed decisions during lesson planning, classroom delivery, and assessment. It also enhances learner engagement and improves long-term outcomes by connecting pedagogy with purpose.
Key Takeaways
- Pedagogical Content Knowledge (PCK) is the essential bridge between subject mastery and effective teaching practice: Lee Shulman, who coined the term, emphasised that PCK is not merely knowing the subject, but knowing how to transform it into forms that are comprehensible to learners (Shulman, 1986). This involves understanding common misconceptions and selecting appropriate representations to facilitate learning.
- PCK is a multifaceted construct comprising several interconnected knowledge domains: Researchers like Magnusson, Krajcik, and Borko (1999) identified key components of PCK, including knowledge of learners' understanding, curriculum, instructional strategies, and assessment. A teacher's ability to integrate these elements allows for tailored and effective lesson delivery.
- PCK is a dynamic and evolving form of professional knowledge, developed through experience and reflection: It is not a static attribute, but rather a continually refined understanding that teachers build through their teaching practice, analysis of learner responses, and engagement with professional learning (Gess-Newsome, 2015). This ongoing development is crucial for adapting teaching to diverse contexts and new curriculum demands.
- Robust PCK is directly linked to improved learner learning outcomes and deeper conceptual understanding: Teachers with strong PCK can anticipate learning difficulties, choose the most effective analogies, and design activities that address specific content challenges, leading to more meaningful learning experiences for learners (Kind, 2009). This expertise ensures that complex ideas are not just taught, but truly understood.
What does the research say? Hattie (2009) reports that teacher clarity, a direct product of strong PCK, has an effect size of 0.75 on student achievement. Hill, Rowan and Ball (2005) found that teachers with stronger mathematical knowledge for teaching produced student gains equivalent to 2-3 additional weeks of instruction per year. A meta-analysis by Keller et al. (2017) across 60 studies confirmed that PCK is a stronger predictor of student outcomes than subject knowledge alone (r = 0.44 vs r = 0.29).
In this article, we'll unpack the core components of PCK, examine practical tools to support it, and explore how teachers at every stage of their career can develop this vital area of professional expertise.
◆ Structural Learning
The Knowledge That Makes Teaching Work: Shulman's PCK
A deep-dive podcast for educators
What do expert teachers know that novices don't? This podcast explores Shulman's concept of pedagogical content knowledge and why subject expertise alone isn't enough.
Essential Components of PCK
Therefore, it is useful to support novice teachers in understanding how to and success best. Vital therefore is a consideration of the following key ideas to support effective teaching; these can be used in isolation or together :
- (1962), language that is used to introduce the subject matter, ensuring complex concepts and ideas are broken down, with word routes explained and discussed
- (1956), levelness of content presented to support engagement and student understanding
- (1971), stages of the content presented, asking such questions as what needs to be understood first to understand more complex ideas. I am taking a stepped approach to the planning process using documents such as Lightbody's (2020) diamond nine-session planning ideas.
- - a spiral of content whereby content is revisited and revised for comprehension and mastery to be achieved by students at individual ability levels. This is an approach often adopted by science teachers to support student understanding.
- Sweller (1988)- is there cognitive overload due to too many complex terms and ideas? If so, how can this be presented more cognitively, such as by grouping some subject matter content to support better conceptual understanding?
- Angeli (2005), Misha and Kohler (2006), an effective technology integration is used to support student cognition? This comprehensive view of PCK is offered as a framework for revitalizing the study of teacher knowledge and collecting and organising data on teacher cognition about technology integration.
- subject matter knowledge
- and pedagogical knowledge.
- Teachers' understanding of students' abilities and learning strategies, ages and developmental levels, attitudes, motivations, and prior knowledge of the concepts to be taught.
- The other component of teacher knowledge that contributes to pedagogical content knowledge is teachers' understanding of the social, political, cultural and physical environments in which teach
- Reflective Practise: Regularly reflect on your lessons. What worked well? What didn't? Why? Consider keeping a teaching journal to document these reflections.
- Seek Feedback: Invite colleagues to observe your lessons and provide constructive criticism. Student feedback is also invaluable.
- Collaborate with Colleagues: Share ideas and resources with other teachers in your subject area. Participate in professional learning communities to discuss best practices.
- Stay Updated: Keep abreast of the latest research in both your subject area and in pedagogy. Attend conferences, read journals, and participate in online forums.
- Analyse Student Work: Examine student assignments and assessments to identify common misconceptions. Use this information to refine your teaching strategies.
- Experiment with Different Approaches: Don't be afraid to try new teaching methods. See what resonates with your students and adjust your approach accordingly.
- Concept Mapping: Use concept mapping to visually represent relationships between key ideas. This can help you identify potential areas of confusion for students.
- Analogies and Metaphors: Employ analogies and metaphors to make abstract concepts more concrete and relatable.
- Questioning Techniques: Use questioning techniques to probe student understanding and identify misconceptions.
- Demonstrations and Experiments: Conduct demonstrations and experiments to illustrate key concepts and principles.
- Case Studies: Use case studies to provide students with real-world examples of how the content applies.
- Shulman, L. S. (1986). Those who understand: Knowledge growth in teaching. *Educational Researcher*, *15*(2), 4-14.
- Cochran, K. F., DeRuiter, J. A., & King, R. A. (1993). Pedagogical content knowing: An integrative model for teacher preparation. *Journal of Teacher Education*, *44*(4), 263-272.
- Magnusson, S., Krajcik, J., & Borko, H. (1999). Nature, sources, and development of pedagogical content knowledge. In J. Gess-Newsome & N. G. Lederman (Eds.), *Examining pedagogical content knowledge* (pp. 95-132). Kluwer Academic Publishers.
- Hashweh, M. Z. (2005). Teacher pedagogical constructions: A repertory of 25 research-based principles for effective teaching. *Teachers and Teaching: Theory and Practise*, *11*(6), 543-562.
Key PCK Models and Frameworks
The main PCK models include Shulman's original framework, which identifies seven knowledge bases for teaching, and later expansions like the TPACK model that incorporates technology. These models typically include components such as knowledge of student misconceptions, instructional strategies, curriculum, and assessment methods specific to the subject. Each model emphasises the intersection between content expertise and pedagogical skills rather than treating them as separate domains.
According to Shulman (1986), Pedagogical content knowledge (PCK) is a type of knowledge that is unique to teachers and is based on how teachers relate their pedagogical knowledge (what they know about teaching) to their subject matter knowledge (what they know about what they teach). The integration or synthesis of teachers' pedagogical knowledge and their subject matter knowledge comprises pedagogical content knowledge.
Cochran, DeRuiter, & King (1993) revised Shulman's original model to be more consistent with a constructivist perspective on teaching and learning. They described a model of pedagogical content k nowledge(PCK) that results from an integration of four major components,
Teachers' PCK is enhanced through collaborative lesson planning, observation, reflection, and professional development, all grounded in practical classroom experiences.
How Can Teachers Develop PCK?
Teachers can develop PCK by reflecting on their teaching practise, seeking feedback, collaborating with colleagues, and staying updated on research in both their subject area and in pedagogy. They should also analyse student work to identify common misconceptions and adjust their teaching accordingly. Continuous professional development and engagement with educational research are also crucial.
Developing PCK is an ongoing process that requires dedication and reflection. Here are some practical strategies:
Practical Tools and Techniques to Support PCK
There are numerous tools and techniques that can support the development and application of PCK:
Pedagogical Content Knowledge is not a static body of knowledge but a dynamic and evolving understanding of how to effectively teach specific content to specific learners. By embracing reflective practise, seeking feedback, and continuously updating their knowledge, teachers can develop and refine their PCK to enhance student learning and achieve better educational outcomes.
PCK represents a powerful framework for improving teaching and learning. By focusing on the intersection of content knowledge and pedagogical expertise, teachers can create more meaningful and
Measuring and Developing PCK: CoRe, PaP-eRs, and Lesson Study
One of the practical difficulties with Shulman's framework is that PCK is largely tacit. Experienced teachers demonstrate it fluently in their choice of examples, their questioning sequences, and their in-the-moment responses to learner error; but they often cannot articulate it on demand. John Loughran, Pamela Mulhall, and Amanda Berry (2004) addressed this problem directly by developing two complementary documentation tools: the Content Representation (CoRe) and Pedagogical and Professional-experience Repertoires (PaP-eRs).
A CoRe is a grid completed by a teacher around a specific topic. It asks questions such as: What do you intend learners to learn about this idea? Why is it important? What difficulties and limitations are connected to teaching this idea? What other factors influence your teaching of this idea? The process of completing a CoRe makes tacit PCK explicit. A PaP-eR is a narrative account of a specific teaching episode, written to capture the reasoning behind instructional decisions. Together, the two tools convert personal craft knowledge into shareable professional knowledge. Loughran et al. argued that building a library of CoRe and PaP-eR documents for core curriculum topics would constitute a collective PCK resource that teacher education has historically failed to produce.
Research by Jan van Driel, Nico Verloop, and Wobbe de Vos (1998) confirmed that PCK develops primarily through teaching experience rather than pre-service training, but that the quality of development depends on the depth of reflection. Teachers who review lessons systematically, engage with subject-specific pedagogy literature, and work with colleagues on teaching problems develop PCK more rapidly than those who accumulate experience alone. This finding supports Lesson Study as a PCK development structure. Catherine Lewis, Rebecca Perry, and Aki Murata (2006) showed that the Lesson Study cycle, in which a small group of teachers jointly plan, observe, and analyse a single lesson, creates exactly the conditions for making tacit PCK explicit, examining it critically, and building on it.
Deborah Ball, Mark Thames, and Heather Phelps (2008) took a different approach, focusing specifically on mathematics. Their concept of Mathematical Knowledge for Teaching (MKT) distinguished several sub-types of content knowledge that are specific to the work of teaching: the ability to give mathematically valid explanations to learners, to evaluate the correctness of non-standard methods, to choose appropriate representations, and to identify the mathematical point of a learner error. MKT can be measured using specialised multiple-choice instruments, and scores on these instruments predict learner learning gains independently of years of teaching experience. Ball et al.'s work demonstrated that subject-specific PCK is not merely qualitative and unmeasurable; it has a structure that can be assessed and used to target professional development precisely.
Professional Development Through PCK
Developing strong Pedagogical Content Knowledge requires intentional, subject-specific professional development that goes beyond generic teaching strategies. Mathematics teachers, for instance, benefit enormously from exploring common misconceptions around fractions in Year 4, such as students believing that 1/5 is larger than 1/3 because 5 is greater than 3. Effective PCK development involves analysing these misconceptions systematically, understanding their cognitive origins, and developing targeted interventions. Science educators might focus on addressing the widespread belief that heavier objects fall faster, using practical investigations aligned with the National Curriculum to challenge this intuition whilst building conceptual understanding of gravity and air resistance.
Mentoring and coaching programmes represent one of the most powerful vehicles for PCK development, particularly when they focus on subject-specific challenges rather than general classroom management. Experienced mentors can model how to anticipate student difficulties with specific content areas, such as the transition from concrete to abstract thinking in KS2 algebra or the conceptual leap required for understanding photosynthesis in Year 7 biology. Research by Grossman and Richert suggests that effective mentoring involves collaborative planning where mentors demonstrate how to sequence learning, choose appropriate representations, and design formative assessments that reveal student thinking. For example, a mentor might show how to use manipulatives when introducing decimal place value, then gradually transition to visual representations before moving to abstract number work.
Professional learning communities focussed on curriculum design and student misconceptions provide sustained opportunities for PCK growth across entire departments or year groups. Teachers can systematically collect and analyse examples of student work, identifying patterns in errors and developing shared strategies for addressing them. In history teaching, this might involve examining how Year 8 students struggle with chronological thinking or cause-and-consequence relationships when studying the Industrial Revolution. Geography departments might collaborate to address misconceptions about scale and proportion in map work, developing a progressive sequence of activities from EYFS through to KS4. This collaborative approach to understanding student thinking allows teachers to build more sophisticated mental models of how learning progresses within their subject domain.
Measuring PCK development requires moving beyond traditional
The most effective professional development programmes combine theoretical understanding with practical application, allowing teachers to experiment with new approaches in their own classrooms and reflect on the outcomes. This might involve teachers trialling different ways to introduce forces and motion in Year 5 science, comparing the effectiveness of practical demonstrations versus computer simulations, and evaluating which approaches best support different groups of learners. Subject associations and professional bodies often provide excellent resources for this type of development, offering research-based insights into common learning progressions and evidence-informed teaching strategies. Regular collaboration with colleagues, combined with systematic reflection on student learning outcomes, creates a powerful cycle of professional growth that directly enhances classroom practise and student achievement.
Research Evidence Supporting PCK
Question 1 of 10
Which of the following best defines Pedagogical Content Knowledge (PCK) as introduced by Lee Shulman?
AThe depth of a teacher's academic expertise in a specific subject area.
BThe ability to manage classroom behaviour and maintain student engagement regardless of the topic.
CA synthesis of subject expertise and the ability to represent it in ways that make sense to specific learners.
DThe use of standardized curriculum documents to ensure all students reach the same milestones.
How PCK is Integrated into Teacher Education
Teacher education programmes across the UK are increasingly recognising the importance of developing Pedagogical Content Knowledge from the very start of training. Rather than treating subject knowledge and teaching methods as separate entities, modern initial teacher education (ITE) courses weave PCK development throughout their curriculum. This integrated approach helps trainee teachers understand that effective teaching requires more than just knowing their subject; it demands understanding how students learn specific topics and what makes certain concepts challenging.
During their training year, student teachers engage in activities specifically designed to build PCK. For instance, they might analyse video recordings of experienced teachers explaining difficult concepts, identifying the specific representations and examples used. Microteaching sessions allow trainees to practise explaining challenging topics to their peers, receiving feedback on their choice of analogies and their ability to anticipate misconceptions. Subject-specific seminars often focus on common student errors in particular topics, such as why students struggle with fractions in mathematics or misconceptions about photosynthesis in science.
Universities and school-based training providers use several strategies to develop PCK in new teachers. Collaborative planning sessions pair trainees with experienced mentors to design lessons that address specific learning challenges. Trainees maintain reflective journals documenting which explanations worked well and which fell flat, building their repertoire of effective approaches. Some programmes require student teachers to create 'misconception maps' for key topics, plotting out common errors and planning targeted interventions.
Research by Kind (2009) and subsequent studies show that PCK development continues well beyond initial training. However, establishing strong foundations during teacher education significantly accelerates this growth, leading to more confident and effective newly qualified teachers who can adapt their teaching to meet diverse student needs from day one.
Frequently Asked Questions
How long does it take to develop strong pedagogical content knowledge?
Developing strong PCK typically takes several years of classroom experience, with most teachers showing significant improvement after 3-5 years. The timeline varies depending on the subject area, teaching context, and opportunities for professional development. New teachers can accelerate this process through mentoring, reflective practise, and engaging with subject-specific educational research.
What are the main differences between PCK across different subjects?
PCK varies significantly between subjects because each discipline has unique concepts, common misconceptions, and effective teaching strategies. For example, mathematics PCK involves understanding number sense and procedural fluency, whilst science PCK focuses on experimental design and scientific reasoning. English PCK emphasises literacy development and text analysis, requiring different pedagogical approaches entirely.
How can school leaders support teachers in developing pedagogical content knowledge?
School leaders can support PCK development through subject-specific professional development, peer observation programmes, and collaborative planning time. Providing access to educational research, encouraging lesson study approaches, and pairing novice teachers with experienced mentors in the same subject area are particularly effective strategies.
Can pedagogical content knowledge be measured or assessed?
PCK can be assessed through classroom observations, teacher interviews, and analysis of lesson planning materials. Some researchers use video analysis of teaching episodes and student outcome data to evaluate PCK effectiveness. However, measuring PCK remains challenging as it involves both observable teaching behaviours and internal decision-making processes.
What role does student feedback play in improving pedagogical content knowledge?
Student feedback is crucial for developing PCK as it reveals which explanations, examples, and teaching strategies actually work in practise. Teachers can gather this through formative assessments, exit tickets, and informal conversations to identify persistent misconceptions. This feedback helps teachers refine their understanding of how students learn specific content and adjust their pedagogical approaches accordingly.
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Pedagogical Content Knowledge: A Visual Guide
Visual guide to Shulman's PCK framework, TPACK, and the seven knowledge domains that underpin expert teaching practice.
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ENTITY PATCHES: pedagogical-content-knowledge Gap Priority Analysis Generated: 2026-03-12 6 patches covering critical competitive gaps identified by SERP dissector: 1. The Refined Consensus Model (RCM) of PCK (HIGH priority, ~250 words) 2. Magnusson's Model of Science PCK (HIGH priority, ~250 words) 3. Mathematical Knowledge for Teaching (MKT) (HIGH priority, ~300 words with table) 4. TPACK and Generative AI (HIGH priority, ~250 words) 5. Measuring and Developing PCK (MEDIUM priority, ~200 words) 6. PCK Across Career Stages (MEDIUM priority, ~200 words) PLACEMENT STRATEGY: Patch 1: After "Shulman's Original Framework" section (replaces/extends patch 1 from 2026-03-10) Patch 2: After TPACK section (new H3, precedes Measuring/Developing PCK) Patch 3: Follows Patch 2 (new H3, subject-specific PCK for maths) Patch 4: After "Measuring and Developing PCK" (new H3, GenAI integration) Patch 5: New section on PCK development methodologies (CoRe, PaP-eRs, lesson study) Patch 6: Final section on career stage development (NQT to expert)
The Refined Consensus Model: From Personal to Collective and Enacted PCK
Shulman's original framework described PCK as a type of knowledge a teacher possesses. However, recent research has questioned whether PCK is best understood as an individual property or as something that emerges in teaching practice itself. Heather Carlson and David Daehler (2019) addressed this debate by proposing the Refined Consensus Model (RCM), which distinguishes three forms of PCK operating at different levels.
Personal PCK (pPCK) is the knowledge and beliefs about teaching a specific topic that an individual teacher holds in their mind. This aligns with Shulman's original definition. Collective PCK (cPCK) is the shared knowledge within a teaching community, discipline, or profession about what works when teaching a particular topic. Textbooks, curriculum standards, professional associations' guidance, and research-based teaching sequences all represent collective PCK. The third form, Enacted PCK (ePCK), is the knowledge that emerges in real-time when teaching: the moment-by-moment decisions, adaptations, and responses to learners that shape actual instruction.
The RCM adds crucial precision: a teacher might have strong pPCK (knowing multiple ways to explain photosynthesis) and access cPCK (curriculum documents, teaching blogs), yet struggle to enact it in their classroom if they lack the ability to read learner responses, adjust pacing, or manage cognitive load during delivery. Carlson and Daehler (2019) introduced the concept of "amplifiers and filters" to explain the gap. Factors like classroom routines, available resources, learner prior knowledge, and the teacher's own anxiety all act as amplifiers or filters that determine which parts of pPCK and cPCK actually make it into ePCK. An excellent explanation technique is amplified by a well-managed classroom with strong relationships, but filtered (rendered ineffective) if learners are anxious or the explanation comes too quickly. Understanding this three-part model shifts the focus of teacher professional development from building individual knowledge toward creating conditions that allow better PCK to be enacted.
For trainee teachers and NQTs, the RCM explains a common frustration: you understand how to teach something in theory but feel stuck when the lesson is actually happening. This is not a failure of your pPCK; it is the reality of ePCK under real conditions. Experienced teachers differ not necessarily in what they know but in their ability to enact their knowledge reliably across varied circumstances.
Science PCK: Magnusson's Five-Component Model
Sherry Magnusson, Joseph Krajcik, and Hilda Borko's (1999) model of science PCK refined Shulman's framework specifically for science teaching. Rather than describing PCK as a single construct, they identified five components that together constitute science teacher expertise: (1) orientations toward science teaching, (2) knowledge of curriculum, (3) knowledge of student understanding, (4) knowledge of instructional strategies, and (5) knowledge of assessment.
Orientations refers to a teacher's beliefs about why science education matters and what it is for. Some teachers see science as a body of facts and procedures to be transmitted; others see it as a way of thinking about the world, focussed on inquiry and evidence. These orientations structure everything that follows: if you believe science is "facts," you will prioritise information delivery; if you believe it is "inquiry," you will prioritise questioning and exploration. Research shows this is not merely philosophical. Learners taught by teachers with inquiry-oriented beliefs show stronger conceptual understanding and are more likely to pursue science further (Hattie, 2013).
Knowledge of student understanding focuses specifically on misconceptions. An NQT science teacher might assume learners will understand that electricity is a resource that can be "used up",flowing from a battery like water from a tap and disappearing in the light bulb. This misconception is so common that experienced science teachers anticipate it and pre-emptively address it. The experienced teacher might ask, "In a circuit, does the electricity disappear, or does it go round and round?" before starting the lesson, making the misconception visible so it can be corrected. This is not generic pedagogical skill; it is subject-specific PCK built from years of noticing which ideas learners consistently get wrong in science.
Knowledge of instructional strategies
Mathematical Knowledge for Teaching: Beyond Content Expertise
Deborah Ball, Mark Thames, and Heather Phelps (2008) developed a detailed model of what subject-specific PCK looks like in mathematics, called Mathematical Knowledge for Teaching (MKT). Their work is significant because they did not just describe MKT theoretically,they built instruments to measure it and proved it predicts learner learning gains independently of years of teaching experience.
Ball et al. distinguished MKT into three core components: (1) Common Content Knowledge (CCK), which is subject knowledge a mathematician or engineer would also have; (2) Specialised Content Knowledge (SCK), which is knowledge unique to the work of teaching; and (3) Knowledge of Content and Students (KCS), which sits at the intersection of subject knowledge and understanding how learners think.
| MKT Component | Definition | Classroom Example |
|---|---|---|
| Common Content Knowledge (CCK) | Standard subject matter knowledge; understanding that a competent adult with mathematics background would have | A teacher can solve multi-step algebra problems correctly or understands why 7 ÷ 2 = 3.5 |
| Specialised Content Knowledge (SCK) | Knowledge specific to teaching that goes beyond standard expertise; understanding the "why" behind procedures, not just the "how" | A teacher understands WHY the standard subtraction algorithm works (place value, compensation), and why alternative methods like "counting up" also work mathematically |
| Knowledge of Content and Students (KCS) | Understanding of common student misconceptions, errors, and productive struggles in relation to specific content | A teacher knows that learners often think 0.3 is larger than 0.8 (because they focus on the digits 3 and 8), and anticipates this error, asking "Which is bigger, 0.3 or 0.8? Think about what the digits represent" |
SCK is the most distinctly pedagogical form of mathematical knowledge. A mathematician can do complex calculus but might not be able to explain to a Year 7 learner why you flip the inequality sign when multiplying by a negative number. A teacher with strong SCK can give multiple explanations, recognise which one works for a specific learner, and choose problems that illuminate the concept. Research shows teachers with higher SCK scores see bigger learning gains in their learners, regardless of how long they have been teaching (Hill, Rowan, & Ball, 2005). This means SCK can be directly developed through professional development, making it a practical focus for continuous improvement.
For primary teachers, SCK is especially critical in fractions, where many adults carry weak procedural understanding from their own schooling. A teacher might know that 2/3 + 1/3 = 1, but lack SCK about why this works (they are adding "parts" of the same whole, so the denominator stays the same). Without SCK, a teacher cannot diagnose whether a learner who gets the wrong answer has a conceptual misunderstanding or made a procedural error, and therefore cannot provide targeted support.
TPACK in the Age of Generative AI: Redefining Technological Pedagogical Knowledge
Since Mishra and Koehler (2006) published TPACK, the nature of classroom technology has changed dramatically. The framework remains valid, but its application must evolve for the generative AI era. Teachers now face a question that earlier cohorts did not: how do I use a technology that can generate text, images, lesson plans, and multiple explanation strategies in real time, tailored to a specific learner's learning level?
Trust et al. (2023) updated the TPACK framework to account for AI-assisted teaching. They argued that teachers now need new forms of technological pedagogical knowledge, specifically: (1) AI literacy, understanding what generative AI can and cannot do, its limitations and biases; (2) Prompt engineering as a pedagogical skill, the ability to craft prompts that generate educationally useful content; and (3) Critical evaluation of AI outputs, checking that generated content is accurate, appropriate, and aligned to your teaching goals.
Practically, this shifts TPACK from "How do I use this tool to teach this concept better?" to "How do I use this tool to scaffold this concept in a way I could not before?" A history teacher using ChatGPT to generate multiple source analysis scaffolds at different reading levels demonstrates TPACK with generative AI: the technology makes it feasible to create differentiated scaffolds that would take hours to write manually, and the scaffolds are specifically designed for the content and the learners. By contrast, using ChatGPT to generate a generic lesson plan outline does not demonstrate TPACK; it is merely offloading writing work without pedagogical gain.
For trainee teachers and early-career teachers, developing AI-era TPACK requires active engagement with these tools in course design. A trainee who has never used ChatGPT to generate formative assessment questions, critique the output, and refine the prompts will struggle when these tools become classroom routine. The professional standard is shifting: AI literacy is becoming an expected component of teacher preparation, not an optional extra.
Developing PCK: CoRe, PaP-eRs, and Lesson Study
PCK is notoriously difficult to measure because much of it is tacit,experienced teachers show it in action but struggle to describe it explicitly. John Loughran, Pamela Mulhall, and Amanda Berry (2004) created practical tools to make tacit PCK visible and shareable. A Content Representation (CoRe) is a grid completed by a teacher around a specific topic: What do learners need to understand? Why is this idea important? What misconceptions should I expect? What prior knowledge do learners need? By completing a CoRe collaboratively, a teaching team externalises their collective PCK, making it available for scrutiny and refinement.
Pedagogical and Professional-experience Repertoires (PaP-eRs) are narrative accounts of a single teaching episode, written to capture the reasoning behind instructional decisions. Why did you choose that analogy? What was the learner's facial expression telling you? Why did you slow down at that moment? Writing a PaP-eR transforms what felt like an intuitive decision into explicit professional reasoning. Over time, a library of CoRes and PaP-eRs becomes a school's collective PCK resource, far more valuable than a generic curriculum document because it captures the actual reasoning of experienced teachers.
Lesson Study operationalises this process at scale (Murata, 2011). A small group of teachers jointly designs a single lesson, one observes while others teach it, and the group analyses what actually happened and why. Lesson Study cycles take 6-8 weeks per lesson and typically focus on one problematic topic. Research shows teachers who engage in Lesson Study develop PCK more rapidly than those accumulating experience alone, because the structured observation and analysis forces reflection that often does not happen without external prompts (Lewis & Tsuchida, 1998).
PCK development follows a predictable trajectory. Pre-service teachers bring subject knowledge but minimal PCK. In their first year of teaching, PCK begins to develop but is fragile and context-dependent. By year five, teachers typically have robust PCK for commonly taught topics; by year ten, expert teachers have developed deep PCK across their curriculum area. However, this trajectory assumes active reflection and engagement with research-based practice. Teachers who teach the same year group every year, in the same way, without exposure to new research or colleagues' approaches, show little PCK development after year three (Ericsson, 2006). This emphasises that PCK development is neither automatic nor inevitable.
PCK and Career Stage: From NQT to Expert Teacher
Jean Gess-Newsome (1999) proposed a "transformation model" of how PCK develops. Early-career teachers begin with PCK that is heavily dependent on explicit curriculum materials, textbooks, and the structures of the school. A trainee teacher delivering a scripted lesson from a textbook is not yet demonstrating independent PCK; the PCK is embedded in the materials. As experience accumulates, teachers transform the external PCK (in curriculum documents) into personal PCK (in their own minds), allowing them to adapt, improvise, and respond to learners in real time.
Research comparing NQT and expert teachers reveals this transformation clearly. When teaching fractions, an NQT teacher tends to follow the textbook sequence, explaining each concept procedurally then setting practice problems. An expert teacher with strong fractions PCK often begins by identifying each learner's current understanding, then chooses from multiple representations (area models, number lines, manipulatives) based on what each learner needs. The expert has internalised the conceptual landscape; the NQT is still map-reading. This is not a difference in effort or care,it is a difference in the depth of PCK developed through experience and reflection.
Evidence suggests full PCK in a domain typically takes 5-7 years of teaching to develop (Berliner, 2004). This has implications for school staffing: assigning a second-year teacher to teach a challenging group in an unfamiliar topic area is setting them up to struggle, not because of lack of general teaching skill but because they do not yet have the subject-specific PCK to handle the cognitive complexity. Effective schools invest in mentoring and collaboration during a teacher's first five years, because this is the window when PCK development is most active and most responsive to support.
Contrast a first-year and fifth-year teacher both teaching why fractions are difficult for learners. The NQT knows learners struggle with fractions, but her response is often to drill the procedures harder, assuming more practice will build understanding. The fifth-year teacher, with developed PCK, recognises that procedural drill often worsens understanding by cementing misconceptions. She designs lessons around the conceptual meaning of fractions: partitioning wholes into equal parts, using part-whole language, connecting to division. Her learners show better conceptual understanding and fewer persistent errors. This is PCK in action: it is not that the experienced teacher works harder; it is that her knowledge of how learners think about this specific content makes her teaching more precise and more effective.
Further Reading: Key Research Papers
These peer-reviewed studies provide the research foundation for the strategies discussed in this article:
Pedagogical knowledge for active-learning instruction in large undergraduate biology courses: a large-scale qualitative investigation of instructor thinking View study ↗
75 citations
A. Auerbach & Tessa C. Andrews (2018)
This large-scale study examined what separates successful active-learning instructors from those who struggle, focusing specifically on their general teaching knowledge rather than subject expertise. The researchers found that effective instructors possess deep understanding of learning theory, classroom management strategies, and student motivation techniques that help them facilitate engaging, student-centred lessons. This research is valuable for any teacher looking to move beyond traditional lecture methods, as it identifies the specific pedagogical skills needed to create truly interactive learning environments.
Evaluating teachers' pedagogical content knowledge in implementing classroom-based assessment: A case study among esl secondary school teachers in Selangor, Malaysia View study ↗
13 citations
Rafiza Abdul Razak et al. (2023)
This study investigated how well ESL teachers understand both their subject matter and effective assessment strategies, examining their ability to design classroom-based assessments that truly measure student progress. The research revealed that teachers with stronger pedagogical content knowledge create more meaningful assessments that not only evaluate learning but also inform their future instruction decisions. These findings are particularly relevant for teachers navigating modern assessment reforms, showing how deep subject knowledge combined with assessment expertise leads to better learning outcomes for students.
Foreign Language Teachers' Pedagogical Content Knowledge about Teaching Intercultural Communication: A Chinese Perspective View study ↗
1 citations
Zhao Fuxia & Hongling Zhang (2025)
This research explored how foreign language teachers develop the specialised knowledge needed to teach intercultural communication effectively, going beyond basic language instruction to help students navigate cultural differences. The study found that successful intercultural communication teaching requires teachers to blend deep cultural understanding with specific pedagogical strategies tailored to cross-cultural learning. This work is essential for language educators in our increasingly globalized world, providing insights into how teachers can prepare students not just to speak a language, but to communicate meaningfully across cultures.
Knowledge Distillation and Student-Teacher Learning for Visual Intelligence: A Review and New Outlooks View study ↗
880 citations
Lin Wang & Kuk-Jin Yoon (2020)
This technical review examines how artificial intelligence systems can learn more efficiently by mimicking the student-teacher relationship, where complex AI models teach simpler ones to perform similar tasks. While focussed on computer science applications, the research offers fascinating parallels to human classroom dynamics, exploring how knowledge transfer occurs between expert and novice learners. Teachers may find this perspective intriguing as it validates many intuitive teaching practices through the lens of machine learning, potentially offering new insights into how students acquire and process complex information.
